Semantic inpainting or image completion alludes to the task of inferring arbitrary large missing regions in images based on image semantics. Since the prediction of image pixels requires an indication of high-level context, this makes it significantly tougher than image completion, which is often more concerned with correcting data corruption and removing entire objects from the input image. On the other hand, image enhancement attempts to eliminate unwanted noise and blur from the image, along with sustaining most of the image details. Efficient image completion and enhancement model should be able to recover the corrupted and masked regions in images and then refine the image further to increase the quality of the output image. Generative Adversarial Networks (GAN), have turned out to be helpful in picture completion tasks. In this chapter, we will discuss the underlying GAN architecture and how they can be used used for image completion tasks.

翻译：语义修复或图像补全是根据图像语义推断任意大面积缺失区域的任务。由于图像像素的预测需要高层上下文的指示，这使得该任务比图像修复更为困难——后者通常更关注修正数据损坏或从输入图像中移除整个物体。另一方面，图像增强旨在消除图像中不希望的噪声和模糊，同时保留大部分图像细节。高效的图像补全与增强模型应能够恢复图像中受损和被遮蔽的区域，并进一步优化图像以提升输出质量。生成对抗网络已被证明在图像补全任务中具有实用性。本章将讨论生成对抗网络的基本架构及其在图像补全任务中的应用。